情感倾向性判断是指根据文本表述分析文本的倾向性,即发表文本的作者所持有的支持或反对的态度,对于特定领域的情感倾向性研究尤以运用监督分类方法所得出的实验结果较为理想。但若将此类方法直接运用于不同领域的文本,其效果却难以尽如人意。在这种情况下,如何利用已标注情感倾向性的源领域文本去判断未知情感倾向性的目标领域文本的倾向性,即跨领域的情感倾向性分析问题——成为当前研究的热点。为此,该文提出一种基于SimRank的跨领域情感倾向性分析算法,把在源领域和目标领域中共现的词汇作为连接两个领域的桥梁,利用情感词典和SimRank算法找出潜在情感空间,然后使用SVM对已标注的源领域进行训练进而得到训练模型,以便利用此模型预测目标领域的情感倾向性。该文亦通过相关实验所得到的实验结果表明了此方法的有效性。

Sentiment classification aims to give the orientation of the review. Much work has been done in opinion mining in a special domain and their results show that the supervised methods performs well. However,such built models are not so good when directly applied to heterogeneous domains. Therefore, the cross domain sentiment classification are currently emphasized so as to predict the opinion of the unlabelled review in one domain by making use of the labeled text from another domain. For this purpose,this paper proposes an algorithm via SimRank to connect the source domain and target domain via the common words between them to build latent emotional space with the help of sentimental dictionary. Thus it enable the prediction of the target review by the model trained on the labeled source domain via SVM. Experimental results show the validation of this method.
Key wordscross domain; sentiment classification;SimRank;SVM